Graph matching refers to establishing node correspondences between two (or among multiple) graphs [1]},
such that the similarity between the matched graphs is maximized.
Some researches focus on the accuracy of the node correspondence,
and regard graph matching as a quadratic assignment programming (QAP) problem [2]}, which is NP-complete.
Thus, researchers mainly employ approximate techniques to seek inexact solutions, such as spectral approximation [3]},
double-stochastic approximation [4]},
and learning-based approaches [1]}, [6]}, [7]}.
While others care about the similarity calculation between graphs.
Early efforts are mainly based on heuristic rules, such as minimal graph edit distance [8]}
and graph kernel methods [9]}, [10]}.
With the development of GNNs, recent work leverage message passing techniques to explore neural-based graph matching [11]}, [12]}.
In this work, we apply neural graph matching for GNN pre-training, to learn adaptive graph representations
and encourage GNNs to integrate localized and globalized domain-specific features.
